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Multimodal Analytics for Next-Generation Big Data Technologies and Applications

  • Book
  • © 2019

Overview

Editors:
  1. Kah Phooi Seng

    1. School of Engineering and Information Technology, University of New South Wales, Canberra, Australia

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  2. Li-minn Ang

    1. School of Information and Communication Technology, Griffith University, Gold Coast, Australia

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  3. Alan Wee-Chung Liew

    1. School of Information and Communication Technology, Griffith University, Gold Coast, Australia

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  4. Junbin Gao

    1. The University of Sydney Business School, University of Sydney, Sydney, Australia

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  • Explains multimodality data analytics in big data environments

  • Important techniques applied to image and speech processing, multimodal information processing, data science, and artificial intelligence

  • Valuable for researchers, professionals and students in engineering, and computer science

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Table of contents (15 chapters)

  1. Front Matter

    Pages i-xv
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  2. Introduction

    1. Front Matter

      Pages 1-1
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    2. Multimodal Information Processing and Big Data Analytics in a Digital World

      • Kah Phooi Seng, Li-minn Ang, Alan Wee-Chung Liew, Junbin Gao
      Pages 3-9

  3. Sentiment, Affect and Emotion Analysis for Big Multimodal Data

    1. Front Matter

      Pages 11-11
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    2. Speaker-Independent Multimodal Sentiment Analysis for Big Data

      • Erik Cambria, Soujanya Poria, Amir Hussain
      Pages 13-43

    3. Multimodal Big Data Affective Analytics

      • Nusrat Jahan Shoumy, Li-minn Ang, D. M. Motiur Rahaman
      Pages 45-71

    4. Hybrid Feature-Based Sentiment Strength Detection for Big Data Applications

      • Yanghui Rao, Haoran Xie, Fu Lee Wang, Leonard K. M. Poon, Endong Zhu
      Pages 73-91

  4. Unsupervised Learning Strategies for Big Multimodal Data

    1. Front Matter

      Pages 93-93
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    2. Multimodal Co-clustering Analysis of Big Data Based on Matrix and Tensor Decomposition

      • Hongya Zhao, Zhenghong Wei, Hong Yan
      Pages 95-124

    3. Bi-clustering by Multi-objective Evolutionary Algorithm for Multimodal Analytics and Big Data

      • Maryam Golchin, Alan Wee-Chung Liew
      Pages 125-150

    4. Unsupervised Learning on Grassmann Manifolds for Big Data

      • Boyue Wang, Junbin Gao
      Pages 151-180

  5. Supervised Learning Strategies for Big Multimodal Data

    1. Front Matter

      Pages 181-181
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    2. Multi-product Newsvendor Model in Multi-task Deep Neural Network with Norm Regularization for Big Data

      • Yanfei Zhang
      Pages 183-205

    3. Recurrent Neural Networks for Multimodal Time Series Big Data Analytics

      • Mingyuan Bai, Boyan Zhang
      Pages 207-243

    4. Scalable Multimodal Factorization for Learning from Big Data

      • Quan Do, Wei Liu
      Pages 245-268

  6. Multimodal Big Data Processing and Applications

    1. Front Matter

      Pages 269-269
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    2. Big Multimodal Visual Data Registration for Digital Media Production

      • Hansung Kim, Adrian Hilton
      Pages 271-297

    3. A Hybrid Fuzzy Football Scenes Classification System for Big Video Data

      • Song Wei, Hani Hagras
      Pages 299-318

    4. Multimodal Big Data Fusion for Traffic Congestion Prediction

      • Taiwo Adetiloye, Anjali Awasthi
      Pages 319-335

    5. Parallel and Distributed Computing for Processing Big Image and Video Data

      • Praveen Kumar, Apeksha Bodade, Harshada Kumbhare, Ruchita Ashtankar, Swapnil Arsh, Vatsal Gosar
      Pages 337-360
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Keywords

About this book

This edited book will serve as a source of reference for technologies and applications for multimodality data analytics in big data environments. After an introduction, the editors organize the book into four main parts on sentiment, affect and emotion analytics for big multimodal data; unsupervised learning strategies for big multimodal data; supervised learning strategies for big multimodal data; and multimodal big data processing and applications.

The book will be of value to researchers, professionals and students in engineering and computer science, particularly those engaged with image and speech processing, multimodal information processing, data science, and artificial intelligence.

Editors and Affiliations

  • School of Engineering and Information Technology, University of New South Wales, Canberra, Australia

    Kah Phooi Seng

  • School of Information and Communication Technology, Griffith University, Gold Coast, Australia

    Li-minn Ang, Alan Wee-Chung Liew

  • The University of Sydney Business School, University of Sydney, Sydney, Australia

    Junbin Gao

Bibliographic Information

  • Book Title: Multimodal Analytics for Next-Generation Big Data Technologies and Applications

  • Editors: Kah Phooi Seng, Li-minn Ang, Alan Wee-Chung Liew, Junbin Gao

  • DOI: https://doi.org/10.1007/978-3-319-97598-6

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer Nature Switzerland AG 2019

  • Hardcover ISBN: 978-3-319-97597-9Published: 30 July 2019

  • eBook ISBN: 978-3-319-97598-6Published: 18 July 2019

  • Edition Number: 1

  • Number of Pages: XV, 391

  • Number of Illustrations: 41 b/w illustrations, 109 illustrations in colour

  • Topics: Artificial Intelligence

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